International Journal of Modern Physics C

The increase of existing computational capabilities has made simulation emerge as a third discipline of Science, lying midway between experimental and purely theoretical branches [G. Makov, C. Gattinoni and A. D. Vita, Model. Simul. Mater. Sci. Eng.17, 084008 (2009); C. J. Cramer, Essentials of Computational Chemistry: Theories and Models, 2nd edn. (John Wiley & Sons, Chichester, 2002)]. Simulation enables the evaluation of quantities which otherwise would not be accessible, helps to improve experiments and provides new insights on systems which are analyzed [T. C. Germann, K. Kadau and S. Swaminarayan, Concurrency Comput. Pract. Exp.21, 2143 (2009); M. A. L. Marques, X. Lopez, D. Varsano, A. Castro and A. Rubio, Phys. Rev. Lett.90, 258101 (2003); D. E. Shaw, P. Maragakis, K. Lindorff-Larsen, S. Piana, R. O. Dror, M. P. Eastwood, J. A. Bank, J. M. Jumper, J. K. Salmon, Y. Shan and W. Wriggers, Science330, 341 (2010); D. Marx, Chem. Phys. Chem.7, 1848 (2006)]. Knowing the fundamentals of computation can be very useful for scientists, for it can help them to improve the performance of their theoretical models and simulations. This review includes some technical essentials that can be useful to this end, and it is devised as a complement for researchers whose education is focused on scientific issues and not on technological respects. In this document, we attempt to discuss the fundamentals of high performance computing (HPC) [G. Hager and G. Wellein, Introduction to High Performance Computing for Scientists and Engineers, 1st edn. (CRC Press, Taylor & Francis Group, 2011)] in a way which is easy to understand without much previous background. We sketch the way standard computers and supercomputers work, as well as discuss distributed computing and discuss essential aspects to take into account when running scientific calculations in computers.

An evacuation was studied from a classroom by means of experiment and simulation. In the experiments, evacuation with and without visibility was mimicked by requiring the evacuees to wear eye masks or not. The distribution of evacuees' egress times against initial positions and the flow rate at exits were studied. It was found that when masks were used, evacuees' egress strategies were highly dependent on their pre-perceived environmental information in subconsciousness which might affect the egress process. Thus we call this phenomenon the "subconscious environmental information perceiving behavior." In the simulation, a cellular automata model considering the influence of sound information and the subconscious behavior was used to simulate the experiments. Both the experimental and the simulation results show that the sound information plays a more important role in evacuation without visibility than in normal condition, and the pre-perceived environmental information is also very important when people have poor visibility because of the subconscious environmental information perceiving behavior. The simulation results consist with the experimental results well. This study is useful for understanding the human behaviors during emergency evacuation with poor visibility under the guide of sound signal.

Community structure is another important feature besides small-world and scale-free property of complex networks. Communities can be coupled through specific fixed links between nodes, or occasional encounter behavior. We introduce a model for opinion evolution with multiple cluster-coupled patterns, in which the interconnectivity denotes the coupled degree of communities by fixed links, and encounter frequency controls the coupled degree of communities by encounter behaviors. Considering the complicated cognitive system of people, the CODA (continuous opinions and discrete actions) update rules are used to mimic how people update their decisions after interacting with someone. It is shown that, large interconnectivity and encounter frequency both can promote consensus, reduce competition between communities and propagate some opinion successfully across the whole population. Encounter frequency is better than interconnectivity at facilitating the consensus of decisions. When the degree of social cohesion is same, small interconnectivity has better effects on lessening the competence between communities than small encounter frequency does, while large encounter frequency can make the greater degree of agreement across the whole populations than large interconnectivity can.

We present results from our quantitative study of statistical and network properties of literary and scientific texts written in two languages: English and Polish. We show that Polish texts are described by the Zipf law with the scaling exponent smaller than the one for the English language. We also show that the scientific texts are typically characterized by the rank-frequency plots with relatively short range of power-law behavior as compared to the literary texts. We then transform the texts into their word-adjacency network representations and find another difference between the languages. For the majority of the literary texts in both languages, the corresponding networks revealed the scale-free structure, while this was not always the case for the scientific texts. However, all the network representations of texts were hierarchical. We do not observe any qualitative and quantitative difference between the languages. However, if we look at other network statistics like the clustering coefficient and the average shortest path length, the English texts occur to possess more clustered structure than do the Polish ones. This result was attributed to differences in grammar of both languages, which was also indicated in the Zipf plots. All the texts, however, show network structure that differs from any of the Watts–Strögatz, the Barabási–Albert, and the Erdös–Rényi architectures.

This work deals with a discussion of complex dynamics of the elementary cellular automaton rule 54. An equation which shows some degree of self-similarity is obtained. It is shown that rule 54 exhibits Bernoulli shift and is topologically mixing on its closed invariant subsystem. Finally, many complex Bernoulli shifts are explored for the finite symbolic sequences with periodic boundary conditions.

A car-following model which involves the effects of traffic interruption probability is further investigated. The stability condition of the model is obtained through the linear stability analysis. The reductive perturbation method is taken to derive the time-dependent Ginzburg–Landau (TDGL) equation to describe the traffic flow near the critical point. Moreover, the coexisting curve and the spinodal line are obtained by the first and second derivatives of the thermodynamic potential, respectively. The analytical results show that considering the interruption effects could further stabilize traffic flow.

Permeability is an important parameter for characterizing the transport properties (e.g. heat and mass transfer) of porous media. It is one of the crucial issues that the permeability of porous media is exactly and quickly decided in many fields such as reservoir engineering, groundwater engineering and composite material modeling. Spontaneous imbibition is a fundamental and ubiquitous natural phenomenon extensively existing in a variety of processes. In this paper, the relationships between the height and weight of imbibition versus the time are derived based on Darcy's law, and a simple method for predicting effective permeability of porous media using spontaneous imbibition effect is proposed, including expressions for permeabilities of artificial and natural porous media. The validity of the proposed models is analysed and tested by experimental data.

The purpose of this paper is to establish a direct methanol fuel cell (DMFC) prediction model by using the support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter selection. Two variables, cell temperature and cell current density were employed as input variables, cell voltage value of DMFC acted as output variable. Using leave-one-out cross-validation (LOOCV) test on 21 samples, the maximum absolute percentage error (APE) yields 5.66%, the mean absolute percentage error (MAPE) is only 0.93% and the correlation coefficient (R²) as high as 0.995. Compared with the result of artificial neural network (ANN) approach, it is shown that the modeling ability of SVR surpasses that of ANN. These suggest that SVR prediction model can be a good predictor to estimate the cell voltage for DMFC system.